I built a Zero-Allocation C# Knowledge Graph (because JVM graphs are too bloated)
Ian Cowley has developed Glacier.Graph, a zero-dependency C# Knowledge Graph aimed at overcoming the limitations of traditional Java-based graph databases. This new graph engine utilizes a Forward Star representation to achieve high performance without the overhead of object-oriented programming. It allows for rapid traversal and persistence of large graphs, significantly improving efficiency for AI applications.
- ▪Glacier.Graph can persist 230,000 edges to disk in just 30 milliseconds.
- ▪The engine uses flat, primitive integer arrays instead of objects to avoid memory allocation issues.
- ▪Traversing a graph of 150,000 nodes took only 5.3 milliseconds to find a path.
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/iancowley/i-built-a-zero-allocation-c-knowledge-graph-because-jvm-graphs-are-too-bloated-4pej |
| Publication time | Mon, 18 May 2026 11:53:55 +0000 |
| Retrieval time | 2026-05-18T12:04:56.488Z |
| Last seen | 2026-05-18T12:04:56.488Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | LmZ8sZcfQS-4 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3928889) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ian Cowley Posted on May 18 I built a Zero-Allocation C# Knowledge Graph (because JVM graphs are too bloated) #ai #dotnet #performance #database If you are building AI agents, you eventually hit the "Memory Wall". Your agent doesn't just need semantic text chunks (Vector Search) or structured tables (SQL). It often needs to trace relationships. For example: Find all suppliers connected to this failing part, or Find the common connection between User_A and User_B.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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